Researchers have shown that neural similarity among humans predicts social closeness and cooperative success, whereas innovation often emerges from interactions among dissimilar individuals. We investigate whether these principles extend to artificial intelligence by examining interactions between large language models. In our experiments, 276 model pairs interact across eight games spanning both cooperation and novelty. We find that pairs with more similar representation spaces achieve significantly higher cooperation but exhibit reduced novelty and creativity. The effects of representational similarity on cooperation and novelty remain robust even after controlling for other factors such as performance disparity and model size. We also find that similarity in the early layers consistently shows the strongest association with cooperation and novelty, compared to the middle and later layers. This suggests that a central factor underlying these patterns could be the extent to which the two models share lexical and semantic grounding. Overall, representational similarity can be an important consideration in multi-agent system design.

翻译：研究人员发现，人类之间的神经相似性可预测社交亲近度与合作成功，而创新往往源于不同个体之间的互动。我们通过考察大型语言模型间的交互，探究这些原理是否适用于人工智能领域。在我们的实验中，276组模型对在涵盖合作与创新维度的八种博弈场景中展开交互。研究发现，表征空间更相似的模型对能实现显著更高的合作水平，但表现出较低的新颖性与创造力。即使在控制性能差异、模型规模等其他因素后，表征相似性对合作与创新的影响依然稳健。我们还发现，与中间层和深层相比，早期层的相似性与合作及创新始终呈现最强关联。这表明，两种模型共享词汇与语义基础的程度可能是驱动这些现象的核心因素。总体而言，表征相似性应成为多智能体系统设计中的重要考量因素。